A Comprehensive Map of the Human Urinary Proteome - Journal of

ARTICLE pubs.acs.org/jpr

A Comprehensive Map of the Human Urinary Proteome Arivusudar Marimuthu,†,‡ Robert. N. O’Meally,§ Raghothama Chaerkady,†,||,^ Yashwanth Subbannayya,†,# Vishalakshi Nanjappa,† Praveen Kumar,† Dhanashree S. Kelkar,†,z Sneha M. Pinto,† Rakesh Sharma,†,0 Santosh Renuse,†,||,^,z Renu Goel,†,b Rita Christopher,0 Bernard Delanghe,4 Robert. N. Cole,§ H. C. Harsha,† and Akhilesh Pandey*,||,^,1,$ †

Institute of Bioinformatics, International Technology Park, Bangalore 560066, Karnataka, India Manipal University, Manipal 576104, Karnataka, India § Mass Spectrometry and Proteomics Facility, Department of Biological Chemistry, Johns Hopkins University School of Medicine, Baltimore 21205, Maryland, United States McKusick-Nathans Institute of Genetic Medicine and ^Department of Biological Chemistry, Johns Hopkins University School of Medicine, Baltimore 21205, Maryland, United States # Rajiv Gandhi University of Health Sciences, Bangalore 560041, Karnataka, India z School of Biotechnology, Amrita University, Kollam 690525, Kerala, India 0 Department of Neurochemistry, National Institute of Mental Health and Neuro Sciences, Bangalore 560006, Karnataka, India b Department of Biotechnology, Kuvempu University, Shimoga 577451, Karnataka, India 4 Thermo Fisher Scientific (Bremen) GmbH, Bremen, Germany 1 Departments of Oncology and $Pathology, Johns Hopkins University School of Medicine, Baltimore 21205, Maryland, United States

)

‡

bS Supporting Information ABSTRACT: The study of the human urinary proteome has the potential to offer significant insights into normal physiology as well as disease pathology. The information obtained from such studies could be applied to the diagnosis of various diseases. The high sensitivity, resolution, and mass accuracy of the latest generation of mass spectrometers provides an opportunity to accurately catalog the proteins present in human urine, including those present at low levels. To this end, we carried out a comprehensive analysis of human urinary proteome from healthy individuals using high-resolution Fourier transform mass spectrometry. Importantly, we used the Orbitrap for detecting ions in both MS (resolution 60 000) and MS/MS (resolution 15 000) modes. To increase the depth of our analysis, we characterized both unfractionated as well as lectin-enriched proteins in our experiments. In all, we identified 1823 proteins with less than 1% false discovery rate, of which 671 proteins have not previously been reported as constituents of human urine. This data set should serve as a comprehensive reference list for future studies aimed at identification and characterization of urinary biomarkers for various diseases. KEYWORDS: body fluids, biomarkers, glycoproteins

’ INTRODUCTION Urine is formed through filtration of plasma by glomeruli in the kidneys. The glomeruli act as a filter to retain most of the proteins.1
3 Ultimately, the excreted urine contains water, glucose, salt and other metabolites in addition to small amounts of proteins, which can be derived from the serum proteins or from the kidney itself.3 From a proteomic standpoint, the urine therefore can provide readout of the systemic physiology (from serum proteins) as well as local physiology (from the proteins contributed by kidneys). Several studies have been carried out in the past employing different approaches to characterize the urinary proteome.4,5 r 2011 American Chemical Society

One of the earliest studies to characterize the urinary proteome employed two-dimensional gel-based electrophoresis and reported 250 spots.6 Decades after this initial study, another group observed 1400 spots in urine employing two-dimensional gel electrophoresis but could identify only 30% of the proteins observed in the gel.7 In subsequent studies, two-dimensional gel electrophoresis followed by MALDI-TOF mass spectrometry resulted in the identification of 120
150 proteins in urine.8,9 Subsequently, studies employing LC
MS/MS have contributed Received: December 15, 2010 Published: April 18, 2011 2734

dx.doi.org/10.1021/pr2003038 | J. Proteome Res. 2011, 10, 2734–2743

Journal of Proteome Research to a significant increase in the number of identified proteins in urine.10
14 However, the aforementioned studies were still limited by the sensitivity of the mass spectrometers used. The study by Adachi et al.,15 and a recent study by Li et al. 16 where they have used LTQ-Orbitrap mass spectrometer, led to the identification of more than 1000 proteins in urine. In both of these studies, although the MS detection was carried out at high resolution in the Orbitrap, the MS/MS detection was performed at a much lower resolution in the linear ion-trap. From these studies, it is clear that we do not yet have a comprehensive view of the urinary proteome. It is important to have a good baseline reference proteome because urine can serve as an excellent body fluid for identification of biomarkers for several reasons. First, collection of urine is a noninvasive procedure. Second, the composition of urine is less complex as compared to serum or plasma, which makes it more attractive for mass spectrometric analysis.1,17 A number of studies have been carried out to identify urinary markers in various disease conditions and candidate biomarkers have been identified for several kidney related disorders including diabetic nephropathy,18,19 acute renal injury and obstructive nephropathy.17 In addition to these renal disorders, potential biomarkers for systemic illnesses have also been identified from urine including prostate cancer,20 bladder cancer21 and colon cancer.22 In this study, we report the largest catalog of proteins in urine identified in a single study to date. This was achieved by an indepth analysis using high-resolution Fourier transform mass spectrometry using high-resolution settings at both MS and MS/MS levels. The analysis was carried using samples pooled from healthy individuals. The use of pooled sample is still arguable, as the normal urine concentration varies from one individual to another.23 However, in this study, we decided to pool urine from multiple individuals to account for the heterogeneity and to obtain a comprehensive coverage of urinary proteome from healthy subjects. This study is an attempt to discover the maximum number of identifiable proteins in urine. We report 1823 proteins in human urine of which 671 proteins have not been previously reported in urine.

’ MATERIALS AND METHODS Sample Collection and Processing

Twenty four healthy individuals were recruited for the study. Details of the samples used in this study are provided in Supplementary Table 1 (Supporting Information). Informed consent was obtained from all subjects and the study was approved by IRB. Thirty to 50 mL of urine was collected from each individual and stored on ice prior to processing. The samples were centrifuged at 1500 g for 10 min at room temperature. The cleared supernatant was further filtered using 0.22 μm filter (Millipore, Billerica, MA). Twenty milliliters of filtered urine from each individual was concentrated using 3 kDa cutoff filters (Millipore, Billerica, MA). The concentrated urine, that is, the retentate, was stored immediately at
80 C until further use. Equal volumes of concentrated urine samples from the 24 individuals were pooled and protein concentration was determined using Lowry’s assay. Sample Fractionation

Approximately 250 μg of protein from pooled urine sample was resolved by ten percent SDS-PAGE (16 18 cm gel). The

ARTICLE

gel was stained with colloidal coomassie blue (Invitrogen, Carlsbad, CA). Thirty gel bands were excised, destained and subjected to in-gel digestion as previously described.24 Briefly, the excised bands were reduced with 5 mM dithiothreitol in 40 mM ammonium biocarbonate followed by alkylation with 10 mM iodoacetamide in 40 mM ammonium bicarbonate. Digestion was carried out using trypsin (modified sequencing grade; Promega, Madison, WI) at 37 C for 16 h. The peptides were extracted from the gel slices as described previously,24 dried and stored at
80 C until LC
MS/MS analysis. The extract from each gel slice was split in to two and analyzed separately as two independent runs. Lectin Affinity Enrichment

Twelve milligrams of total protein was diluted in 10 mM phosphate buffer pH 7.8. For glycoprotein enrichment, the samples were incubated with a mixture of three agarose conjugated lectins—concanavalin A, wheat germ agglutinin and jacalin (Amersham BioSciences)—for 12 h at 4 C. The beads were then washed three times using wash buffer (10 mM phosphate buffer pH 7.8) and the bound proteins were eluted using a mixture of carbohydrates (100 mM each of N-acetylglucosamine, melibiose and galactose). The eluate was dialyzed against wash buffer to remove free sugars and concentrated using 3 kDa cutoff filters. The concentrated proteins were measured by Lowry’s method. One fourth (500 μg from 12 mg) of the proteins were found to be enriched using lectin affinity chromatography. Half the amount of the concentrated protein (250 μg) was then resolved by SDS-PAGE and visualized using colloidal coomassie staining. Twenty-four bands were excised and subjected to in-gel trypsin digestion procedure as previously described.24 The extract from each gel slice was split in to two and analyzed separately as two independent runs. LC
MS/MS analysis

Nanoflow electrospray ionization tandem mass spectrometric analysis of peptide samples was carried out using LTQ-Orbitrap Velos (Thermo Scientific, Bremen, Germany) interfaced with Agilent’s 1200 Series nanoflow LC system. The chromatographic capillary columns used were packed with Magic C18 AQ (Michrom Bioresources, 5 μm particle size, pore size 100 Å) reversed phase material in 100% acetonitrile at a pressure of 1000 psi. The peptide sample from each fractionation method was enriched using a trap column (75 μm  2 cm) at a flow rate of 5 μL/min and separated on an analytical column (75 μm  10 cm) at a flow rate of 300 nL/min. The peptides were eluted using a linear gradient of 7
30% acetonitrile over 50 min. Mass spectrometry analysis was carried out in a data dependent manner with full scans acquired using orbitrap mass analyzer at a mass resolution of 60 000 at 400 m/z. For each cycle, twenty most intense precursor ions from a survey scan were selected for MS/MS and detected at a mass resolution of 15 000 at m/z 400. The fragmentation was carried out using higher-energy collision dissociation as the activation method with 40% normalized collision energy. The ions selected for fragmentation were excluded for 30 s. The automatic gain control for full FT MS was set to 1 million ions and for FT MS/MS was set to 0.1 million ions with a maximum time of accumulation of 750 and 100 ms, respectively. For accurate mass measurements, the lock mass option was enabled. Internal calibration was carried out using the Polydimethylcyclosiloxane (m/z, 445.1200025) ions. 2735

dx.doi.org/10.1021/pr2003038 |J. Proteome Res. 2011, 10, 2734–2743

Journal of Proteome Research

ARTICLE

Figure 1. Workflow employed to characterize the urinary proteome. Pooled urine from healthy individuals was processed by two different approaches. A fraction of pooled urine was resolved by SDS-PAGE without any enrichment. Gel bands were excised followed by in-gel digestion. Another fraction was subjected to lectin affinity chromatography using three lectins (Concanavalin A, wheat germ agglutinin and jacalin) followed by SDS-PAGE and ingel digestion. In-gel digested gel bands from two different approaches were further analyzed by liquid chromatography tandem mass spectrometry (LC
MS/MS) on an LTQ-Orbitrap Velos mass spectrometer. Data derived from mass spectrometric analysis were then searched using Mascot and Sequest search engines.

Data Analysis

The mass spectrometry data was processed using Proteome Discoverer (Version 1.2.0.208) software (Thermo Fisher Scientific) workflow and searched using Mascot and Sequest as search nodes with Xtract feature enabled against NCBI RefSeq release 40 human protein database containing 31 811 sequences. The search parameters used were oxidation of methionine, deamidation at N and Q and protein N-terminal acetylation as variable modifications and carbamidomethylation of cysteine residues as a fixed modification. A maximum of one missed cleavage was allowed for tryptic peptides. The peptide and protein data were extracted using high peptide confidence and top one peptide rank filters. False discovery rate was calculated by enabling the peptide sequence analysis using decoy database. Mass error window of 20 ppm and 0.1 Da were allowed for MS and MS/MS, respectively. 1% FDR was used as a cutoff value for reporting identified peptides. Data Submission

We have submitted the mass spectrometry data from this study in two public repositories. The raw data can be downloaded from Tranche data repository using the following hash code (F1iMznHQJ8þEþyfFk3nYyXTiQOX9IeTtBy/JTiqwpPNM91Uf3h4onhabWYsMLsGaYzFLkNgYod9lZ5iARFH6Pyhm3FkAAAAAAAAXZQ==). The peptide identifications and associated MS/MS spectra are available from Human Proteinpedia25 (HuPA_00672). Bioinformatics Analysis

Human Protein Reference Database (HPRD)26 was used for obtaining annotations pertaining to domains, motifs and posttranslational modifications among the proteins identified. Gene

ontology annotation was carried out by mapping the proteins identified to HPRD database which complies with GO terminologies for functional description of proteins.

’ RESULTS AND DISCUSSION Urine pooled from 24 healthy individuals was used in this study. We employed SDS-PAGE-based separation of unfractionated as well as lectin-enriched urine samples as shown in Figure 1. In total, 54 in-gel bands were analyzed as two replicate runs by LC-MS/MS analysis using high resolution Fourier transform mass spectrometry. The acquired spectra were searched against Human RefSeq release 40 database using Sequest and Mascot search engines. The peptide identifications from Mascot and Sequest were combined into a single list each for both nonenriched and lectin-enriched data sets. A nonredundant list of peptide identifications was then obtained by merging the two lists from nonenriched and enriched data (Supplementary Table 2A, Supporting Information). If a given peptide was identified from both non enriched and enriched set, the one with the highest score was retained in the final compilation of urinary proteome. Supplementary Table 2B and 2C (Supporting Information) also provide a unique list of peptide identifications from enriched and nonenriched data set. A complete list of proteins identified in this study is given in Supplementary Table 3, Supporting Information. LC
MS/MS analysis of 30 in-gel bands from unfractionated urine without lectin-enrichment gave rise to the identification of 1452 proteins. The enrichment of glycoproteins in urine by a lectin-based affinity method using a mixture of three lectins, concanavalin A, jacalin and wheat germ agglutinin, was carried out to increase the depth of our analysis. Since different lectins 2736

dx.doi.org/10.1021/pr2003038 |J. Proteome Res. 2011, 10, 2734–2743

Journal of Proteome Research

ARTICLE

Table 1. Proteomic Studies Carried out on Healthy Human Urinea S.NO total/sub proteome

depletion/enrichment

number of identifications

Albumin depleted

instrument used

citation

1

Total

1543

LTQ-Orbitrap

Adachi et al. (2006)

2

Total

1310

LTQ-Orbitrap

Li et al. (2010)

3

Exosomes

1132

4

Total

5

Total

6

Total

7

Total

8 9

Exosomes Total

10

Total

11

Total

Immunoaffinity subtraction chromatography

12

Total

Combination of LDS-PAGE and 2-DE

13

Total

14

Total

15

Total

16 17

Total Total

LTQ linear ion trap

Gonzales et al. (2009)

600

LTQ

Lee et al. (2008)

383

SELDI-TOF and FT-ICR

Castagna et al. (2005)

339

MALDI-TOF

Khan et al. (2006)

334

LCQ-DECA XP Plus

Wang et al. (2006)

295 245

LCQ-DECA XP LCQ-DECA XP

Pisitkun et al. (2004) Kim et al. (2009)

226

LCQ-DECA XP Plus

Sun et al. (2005)

150

MALDI-TOF and LCQ-DECA Pieper et al. (2004)

141

MALDI-TOF/TOF

Zerefos et al. (2006)

124

Q-TOF

Spahr et al. (2001)

113

MALDI-TOF

Oh et al. (2004)

None

91

LCQ-DECA

Pang et al. (2002)

None

48 47

MALDI- TOF/TOF MALDI-TOF

Smith et al. (2005) Thongboonkerd et al. (2002)

Ligand library beads enrichment Lectin affinity enrichment

a

Summary of the mass spectrometry based studies carried out until date to identify urinary proteome of healthy individuals is listed. Table includes details about depletion or enrichment strategy that was employed, number of protein identifications and the type of mass spectrometer used in the listed studies.

have affinity toward different carbohydrate moieties, the use of multiple lectins in this step ensures a broader coverage of glycoproteins.27 The enriched glycoproteins were subjected to SDS-PAGE. LC
MS/MS analysis of 24 in-gel digested protein bands from the lectin-enriched samples led to the identification of 617 proteins out of which 246 proteins were also identified in the unfractionated urine without lectin-enrichment. A list of all the proteins that were enriched by lectin affinity chromatography in this study is provided in Supplementary Table 4 (Supporting Information). In total, we identified 1823 proteins which include 721 single peptide hits. The single peptide hits were taken in to account only if it maps uniquely to the protein. Fifty percent of those hits were also supported by multiple peptide spectral matches (PSMs). Representative MS/MS spectra for a subset of proteins identified based on single peptide are shown in Supplementary figures (Supplementary Figure 1 and Supplementary Figure 2, Supporting Information). The proteins that were identified in lectin-enriched fractions were mapped to HPRD, which contains manually curated literature-based evidence for post-translational modifications of human proteins.28 Among the 617 proteins that were enriched, 265 proteins were known to be glycosylated based on data from HPRD. Though we did not enrich for acetylated peptides, we could find many peptides that were acetylated. In total, 131 peptides representing protein N-terminus were identified out of which 44 peptides were found to be acetylated. Comparison with Other Studies on Urinary Proteomes

A number of studies have been carried out to characterize the urinary proteome from healthy individuals. Table 1 summarizes the studies carried out to date. We compared the list of proteins identified in our study with two other large-scale studies—one by Adachi et al. that reported 1543 proteins and another by Li et al. that reported 1310 proteins16 (Figure 2). As seen in the figure, our study identified 744 proteins that were not reported by either of these two studies. Similarly, Li et al. identified 308 unique

Figure 2. Venn diagram comparing large scale studies on urinary proteome. Protein identifications from the current study were compared to two other studies (Adachi et al., 2006 and Li et al., 2010) that were carried out using high-resolution mass spectrometers. A total of 744 proteins were unique to this study, whereas 373 proteins were unique to Adachi et al. and 308 were unique to Li et al.

proteins and Adachi et al. identified 373 unique proteins. Proteins identified in common (658) to all the three studies are provided in Supplementary Table 5 (Supporting Information) as it represents the most commonly identifiable proteins in urine. Combining all the data from the studies published to date, there are at least 2500 proteins that have now been reported in the urine. In addition to proteins being secreted in urine, exosomes are released into the urine by renal epithelial cells. Exosomes are known to be released by multiple cell types and have gained more attention after their discovery in many body fluids. Exosomes have been implicated in multiple biological processes including immune response, intracellular communication and transport.29 Large scale analysis of exosomes in urine was carried out by Pisitkun et al. and Gonzales et al.30,31 By comparing our data with proteins identified in urinary exosomes, we found that ∼50% of 2737

dx.doi.org/10.1021/pr2003038 |J. Proteome Res. 2011, 10, 2734–2743

Journal of Proteome Research

ARTICLE

Table 2. Partial List of Proteins Not Previously Reported in Urinea gene symbol

a

name

subcellular localization

molecule class

molecular function

BAI2

Brain specific angiogenesis inhibitor 2

Plasma membrane

Integral membrane protein

Receptor activity

FOLR2

Folate Receptor 2

Plasma membrane

Cell surface receptor

Receptor activity

GPC6

Glypican 6

Plasma membrane

Growth factor

Growth factor activity

LYST

Lysosomal trafficking regulator

Lysosome

Adapter molecule

Receptor signaling complex scaffold activity

ST6GAL1

Sialyltransferase 1

Golgi apparatus

Enzyme: Sialyltransferase

Sialyltransferase activity

LCN6

Lipocalin 6

Extracellular

Unclassified

Molecular function unknown

EGFL6

Epidermal growth factor- like 6

Extracellular

Secreted polypeptide

Molecular function unknown

IGFBP4 MUCL1

IGF binding protein 4 Small breast epithelial mucin

Extracellular Extracellular

Adhesion molecule Secreted polypeptide

Cell adhesion molecule activity Molecular function unknown

NCAN

Neurocan

Extracellular

Extracellular matrix protein

Extracellular matrix structural constituent

SPP2

Secreted phosphoprotein 2, 24-KD

Extracellular

Protease inhibitor

Protease inhibitor activity

TYRP1

Tyrosinase related protein 1

Endosome

Oxidoreductase

Oxidoreductase activity

SHISA5

Scotin

Endoplasmic reticulum

Integral membrane protein

Signal transducer activity

CHMP4A

Charged multivesicular body protein 4a

Cytoplasmic vesicle

Unclassified

Molecular function unknown

CAMKK1

CAMKK alpha protein

Cytoplasm

Serine/threonine kinase

Protein serine/threonine kinase activity

TUSC2 CIT

Lung cancer candidate FUS1 protein Citron

Cytoplasm Cytoplasm

Unclassified Serine/threonine kinase

Molecular function unknown Protein serine/threonine kinase activity

FICD

Huntingtin interacting protein E

Unclassified

Molecular function unknown

MEAF6

Sarcoma antigen NY-SAR-91

Unclassified

Molecular function unknown

TCHH

Trichohyalin

Calcium binding protein

Calcium ion binding

A list of novel proteins with their subcellular localization, molecular class and molecular function are provided.

the proteins identified in our study overlap with that identified in exosomes (Supplementary Table 6, Supporting Information). Comparison of Urinary Proteome with Plasma Proteome

Lulu et al.32 compiled the data from previously reported studies on urine and plasma and compared the proteins in kidney input (plasma) with those in the kidney output (urine). This study classified the proteins into three categories—proteins that were detected in urine, proteins that were detected in plasma and proteins that were detected in both plasma and urine. By comparing the list of proteins that was identified in this study with these three categories, we observed that 154 proteins which were previously thought to be restricted to the plasma proteome were detected in our analysis of the normal urinary proteome. Moreover, more than 90% of these proteins were identified for the first time in this study in urine. In summary, 23% (431/1823) of the proteins identified in urine were in common to the proteins that were reported in plasma. Proteins Not Previously Reported in Urine

Of the 1823 proteins identified in our study, approximately 40% of the proteins were unique to our study. We think that this is likely owing to the fact that we employed an additional enrichment methodology. In fact, 25% of the novel proteins identified in this study have been identified from the lectin enriched fraction. In addition, we used an LTQ-Orbitrap Velos while many of the earlier studies were done on LTQ-Orbitrap or other instruments with a lower sensitivity. The significance of some of the identified proteins is discussed in detail below. A partial list of novel proteins identified in this study is given in Table 2. Representative MS/MS spectra of a subset of peptides identified from some of those proteins are shown in Figure 3. Among the novel proteins detected in urine, we identified a group of voltage gated potassium ion channels which includes potassium voltage gated channel, subfamily E member 4 (KCNE4), potassium channel tetramerising domain containing

12 (KCTD12) and potassium voltage gated channel, subfamily H member 5 (KCNH5). Voltage gated potassium channels are multifunctional and they play a crucial role in electrolyte transport, neuronal excitability and several other functions.33 Germline variants in KCNE4 have been shown to be associated with childhood acute lymphoblastic leukemia.34 KCNH5, has been shown to hypermethylated in lung carcinoma as compared to normal lung tissue.35 Ion channel proteins are an important class of molecules involved in multiple biological functions and their detection in urine might prove to be clinically significant. Shroom family member 2 (SHROOM2) was originally identified as human homologue of APX gene and is expressed in retina and other tissues including lung, kidney and pancreas. Based on its association with actin it has been characterized as a structural protein,36 though the other functions of this protein are yet to be characterized. Shroom2 is known to cause amiloride sensitive sodium channel activity in Xenopus laevis.37 It has also been reported as a strong candidate gene for ocular albinism.36 A representative MS/MS spectrum of a peptide identified from Shroom2 is shown in Figure 3A. Tumor suppressor candidate 1 (TUSC2) is another novel protein which was detected in urine. The gene encoding this protein is known to be deleted resulting in loss of heterozygosity observed in lung cancer cell lines.38 A representative MS/MS spectrum of a peptide identified from TUSC2 is shown in Figure 3B. Functional Classification of Identified Proteins

The proteins identified from urine were classified according to Gene Ontology (Figure 4). The classification based on cellular component (Figure 4A) revealed that the majority of proteins were known to be either extracellular (38%) or present on the plasma membrane (31%). We also identified a number of intracellular proteins that were known to be cytoplasmic (17%), lysosomal (6%) or nuclear (6%). Classification based 2738

dx.doi.org/10.1021/pr2003038 |J. Proteome Res. 2011, 10, 2734–2743

Journal of Proteome Research

ARTICLE

Figure 3. Representative MS/MS spectra of peptides from selected proteins that were identified based on single peptide evidence. (A) Shroom2 (SHROOM2). (B) Tumor suppressor candidate 2 (TUSC2). (C) Kallikrein-11. (D) Folate receptor beta (FOLR2).

on molecular function (Figure 4B) shows that the large majority of the proteins are known to be involved in cell
cell communication (27%), cell growth (15%) or metabolism or energy pathways (18%). Eighteen percent of the proteins were also known to be involved in transport (9%) and immune response (9%). Based on biological process (Figure. 4C), the proteins were classified into those involved in receptor activity (25%), cell adhesion activity (10%), growth factor activity (2%), catalytic activity (10%) and transporter activity (9%) and as part of extracellular matrix structural constituent (7%). Potential Biomarkers Identified in Human Urine

The proteins that we have detected in normal urine could be altered in their expression in various diseases in which case the differential expression of such proteins could be assessed in urine. This could potentially lead to development of sensitive assays as markers for such diseases. An overview of some of the proteins identified in our study that have previously been reported to be altered in abundance in certain disease conditions is provided below. Fatty acid binding proteins (FABPs) are a group of small molecular weight proteins that are involved in lipid metabolism and transport.39 Nearly 10 members of this family of proteins have been identified thus far.39 Among them, FABP1, FABP2, FABP3 and FABP5 were detected in human urine under normal conditions in this study. Few of the proteins that were mentioned above have been described in the context of disease conditions. FABP1 has been described as a biomarker for early

detection of acute kidney injury and chronic kidney disease.40 FABP3 was discovered as a diagnostic marker in cerebrospinal fluid of patients with stroke as well as in the serum of those patients.41 We were able to detect several proteins implicated in various cancers in human urine. For instance, tubulin (TUBB), beta actin (ACTB), and vimentin (VMN) that have been shown to be overexpressed in hepatacellular carcinoma42 were detected in urine. Several other proteins—heat shock 70 kDa protein 8 (HSPA8), annexin A4 (ANXA4), tubulin beta (TUBB2C), 14-3-3 beta (YWHAB)—that have been reported to be overexpressed in renal cell carcinoma43 were detected in urine. We were able to identify proteins like FABP5 and Ezrin (EZR), which are known to be overexpressed in lymph node metastases of prostate cancer.44 We could also detect tumor suppressor candidate 2 (TUSC2) (Figure 3B) in urine, which has been found to be downregulated in lung cancer.45 Kinases are key molecules involved in signaling pathways regulating cellular proliferation, metabolism, differentiation and survival. Identification of these essential proteins in body fluids would lead to novel methods of clinical diagnosis. Some of the kinases that were identified in urine are discussed below. Ephrin receptors are the largest known subfamily of receptor tyrosine kinases. In mammals, Ephrin receptor family comprises of 14 members.46 Ephrin receptors have been shown to act as both tumor promoters and tumor suppressors in different cancers.46 2739

dx.doi.org/10.1021/pr2003038 |J. Proteome Res. 2011, 10, 2734–2743

Journal of Proteome Research

ARTICLE

Figure 4. Gene Ontology based classification of proteins. (A) Cellular component, (B) molecular function and (C) biological process based classification of identified proteins in this study.

We identified a number of ephrin receptor family members
Ephrin receptors A1, A2, A7, A10, B2, B3, B4 and B6 (EPHA1, EPHA2, EPHA7, EPHA10, EPHB2, EPHB3, EPHB4, and EPHB6) in urine. Ephrin receptor A2 (EPHA2) and EPHA7 identified in our study in urine have been shown to be abundantly expressed in pancreatic ductal adenocarcinoma tissues.47 Other kinases that were overexpressed in cancers,48,49,50 and detected in urine include- AXL receptor tyrosine kinase (AXL), PDGF receptor beta (PDGFRB) and fibroblast growth factor receptor 1 (FGFR1). Glycoprotein Markers Identified in Human Urine

A large majority of the secreted and cell surface proteins that are shed into the bloodstream are known to be glycosylated.51 A number of these proteins were detected in the lectin-enriched fraction from urine. Some of the serum glycoproteins51 that were detected in urine include alkaline phosphatase (ALPP), uromodulin (UMOD), carcinoembryonic antigen 5 (CEACAM5) kallikreins 3, 5, 6, 7, 8, 10, and 11 (KLK3, KLK5, KLK6, KLK8, KLK10 and KLK11 (Figure 3C)) and vitamin D binding protein (GC). Apolipoprotein A (APOA), apolipoprotein A2 (APOA2), and haptoglobin (HP), which have been reported to be elevated in the serum of hepatocellular carcinoma patients,42 tissue inhibitor of matrix metalloproteinase 1 (TIMP1), which has been reported to be elevated in the serum of colon cancer patients,52 mac-2 binding protein (LGALS3BP), which has been reported to be elevated in the secretome of esophageal squamous cell carcinoma cells,53 were also detected in the lectin-enriched fraction. Interestingly, we could identify proteins that are associated with neurological disorders in urine. One such protein is folate receptor alpha (FOLR1), which has recently been described in

the context of meningomyelocele, a developmental birth defect caused by the incomplete closure of the neural tube.54 We were able to detect two members of the family in urine—folate receptor 1 (FOLR1) and folate receptor 2 (FOLR2) (Figure 3D). Another protein sortilin (SORT1) which was detected in urine has been described in the context of Alzheimer’s disease.55 It has also been described in association with coronary artery disease56 and myocardial infarction.57

’ CONCLUSIONS We report here the largest catalog of human urinary proteome to date based on high confident peptide identification. This was achieved by the use of high-accuracy and high-resolution mass spectrometer. In spite of urine being available by noninvasive methods, its diagnostic potential has not been exploited except in few cases like pregnancy test and other kidney-related diseases. Findings from this study could provide a basis for biomarker assay developments in various diseases. ’ ASSOCIATED CONTENT

bS

Supporting Information Supplementary Table 1, Details of samples used in this study. Supplementary Table 2, List of all the peptides (2A) peptides from unfractionated urine (2B) and peptides from lectin enriched fraction (2C). Supplementary Table 3, Complete list of proteins identified from this study. Supplementary Table 4, Complete list of proteins identified by lectin affinity enrichment. Supplementary Table 5, List of proteins identified in common to

2740

dx.doi.org/10.1021/pr2003038 |J. Proteome Res. 2011, 10, 2734–2743

Journal of Proteome Research the three large scale studies on urinary proteome. Supplementary Table 6, Proteins identified in common to this study and urinary exosomal proteins reported by Pisitkun et al., 2004 and Gonzales et al., 2009. Supplementary Figures: MS/MS spectra for a subset of proteins identified based on single peptide. This material is available free of charge via the Internet at http://pubs.acs.org.

’ AUTHOR INFORMATION Corresponding Author

*Akhilesh Pandey M.D., Ph.D., McKusick-Nathans Institute of Genetic Medicine, 733 N. Broadway, BRB 527, Johns Hopkins University, Baltimore, MD 21205. Tel.: 410-502-6662. Fax: 410502-7544. E-mail: [email protected]. B.D. is an employee of Thermo Fisher Scientific. All other authors have expressed no conflict of interest.

’ ACKNOWLEDGMENT We thank the Department of Biotechnology (DBT) of the Government of India for research support to the Institute of Bioinformatics, Bangalore. Y.S., D.S.K. and S.R. are recipients of Junior Research fellowship award from the University Grants Commission (UGC) of the Government of India. R.S. is a Research associate supported by DBT. H.G. is a Wellcome Trust/DBT India Alliance Early Career Fellow. ’ REFERENCES (1) Decramer, S.; Gonzalez de Peredo, A.; Breuil, B.; Mischak, H.; Monsarrat, B.; Bascands, J. L.; Schanstra, J. P. Urine in clinical proteomics. Mol. Cell. Proteomics 2008, 7 (10), 1850–62. (2) Oh, J.; Pyo, J. H.; Jo, E. H.; Hwang, S. I.; Kang, S. C.; Jung, J. H.; Park, E. K.; Kim, S. Y.; Choi, J. Y.; Lim, J. Establishment of a nearstandard two-dimensional human urine proteomic map. Proteomics 2004, 4 (11), 3485–97. (3) Thongboonkerd, V.; Malasit, P. Renal and urinary proteomics: current applications and challenges. Proteomics 2005, 5 (4), 1033–42. (4) Lee, R. S.; Monigatti, F.; Briscoe, A. C.; Waldon, Z.; Freeman, M. R.; Steen, H. Optimizing sample handling for urinary proteomics. J. Proteome Res. 2008, 7 (9), 4022–30. (5) Khan, A.; Packer, N. H. Simple urinary sample preparation for proteomic analysis. J. Proteome Res. 2006, 5 (10), 2824–38. (6) Anderson, N. G.; Anderson, N. L.; Tollaksen, S. L. Proteins of human urine. I. Concentration and analysis by two-dimensional electrophoresis. Clin. Chem. 1979, 25 (7), 1199–210. (7) Pieper, R.; Gatlin, C. L.; McGrath, A. M.; Makusky, A. J.; Mondal, M.; Seonarain, M.; Field, E.; Schatz, C. R.; Estock, M. A.; Ahmed, N.; Anderson, N. G.; Steiner, S. Characterization of the human urinary proteome: a method for high-resolution display of urinary proteins on two-dimensional electrophoresis gels with a yield of nearly 1400 distinct protein spots. Proteomics 2004, 4 (4), 1159–74. (8) Kim, K. H.; Moon, M. H. High speed two-dimensional protein separation without gel by isoelectric focusing-asymmetrical flow field flow fractionation: application to urinary proteome. J. Proteome Res. 2009, 8 (9), 4272–8. (9) Smith, G.; Barratt, D.; Rowlinson, R.; Nickson, J.; Tonge, R. Development of a high-throughput method for preparing human urine for two-dimensional electrophoresis. Proteomics 2005, 5 (9), 2315–8. (10) Castagna, A.; Cecconi, D.; Sennels, L.; Rappsilber, J.; Guerrier, L.; Fortis, F.; Boschetti, E.; Lomas, L.; Righetti, P. G. Exploring the hidden human urinary proteome via ligand library beads. J. Proteome Res. 2005, 4 (6), 1917–30. (11) Sun, W.; Li, F.; Wu, S.; Wang, X.; Zheng, D.; Wang, J.; Gao, Y. Human urine proteome analysis by three separation approaches. Proteomics 2005, 5 (18), 4994–5001.

ARTICLE

(12) Spahr, C. S.; Davis, M. T.; McGinley, M. D.; Robinson, J. H.; Bures, E. J.; Beierle, J.; Mort, J.; Courchesne, P. L.; Chen, K.; Wahl, R. C.; Yu, W.; Luethy, R.; Patterson, S. D. Towards defining the urinary proteome using liquid chromatography-tandem mass spectrometry. I. Profiling an unfractionated tryptic digest. Proteomics 2001, 1 (1), 93–107. (13) Zerefos, P. G.; Vougas, K.; Dimitraki, P.; Kossida, S.; Petrolekas, A.; Stravodimos, K.; Giannopoulos, A.; Fountoulakis, M.; Vlahou, A. Characterization of the human urine proteome by preparative electrophoresis in combination with 2-DE. Proteomics 2006, 6 (15), 4346–55. (14) Pang, J. X.; Ginanni, N.; Dongre, A. R.; Hefta, S. A.; Opitek, G. J. Biomarker discovery in urine by proteomics. J. Proteome Res. 2002, 1 (2), 161–9. (15) Adachi, J.; Kumar, C.; Zhang, Y.; Olsen, J. V.; Mann, M. The human urinary proteome contains more than 1500 proteins, including a large proportion of membrane proteins. Genome Biol. 2006, 7 (9), R80. (16) Li, Q. R.; Fan, K. X.; Li, R. X.; Dai, J.; Wu, C. C.; Zhao, S. L.; Wu, J. R.; Shieh, C. H.; Zeng, R. A comprehensive and non-prefractionation on the protein level approach for the human urinary proteome: touching phosphorylation in urine. Rapid Commun. Mass Spectrom. 2010, 24 (6), 823–32 . (17) Thongboonkerd, V. Urinary proteomics: towards biomarker discovery, diagnostics and prognostics. Mol. Biosyst. 2008, 4 (8), 810–5. (18) Rossing, K.; Mischak, H.; Dakna, M.; Zurbig, P.; Novak, J.; Julian, B. A.; Good, D. M.; Coon, J. J.; Tarnow, L.; Rossing, P. Urinary proteomics in diabetes and CKD. J. Am. Soc. Nephrol. 2008, 19 (7), 1283–90. (19) Sharma, K.; Lee, S.; Han, S.; Lee, S.; Francos, B.; McCue, P.; Wassell, R.; Shaw, M. A.; RamachandraRao, S. P. Two-dimensional fluorescence difference gel electrophoresis analysis of the urine proteome in human diabetic nephropathy. Proteomics 2005, 5 (10), 2648–55. (20) Okamoto, A.; Yamamoto, H.; Imai, A.; Hatakeyama, S.; Iwabuchi, I.; Yoneyama, T.; Hashimoto, Y.; Koie, T.; Kamimura, N.; Mori, K.; Yamaya, K.; Ohyama, C. Protein profiling of post-prostatic massage urine specimens by surface-enhanced laser desorption/ ionization time-of-flight mass spectrometry to discriminate between prostate cancer and benign lesions. Oncol. Rep. 2009, 21 (1), 73–9. (21) Gkialas, I.; Papadopoulos, G.; Iordanidou, L.; Stathouros, G.; Tzavara, C.; Gregorakis, A.; Lykourinas, M. Evaluation of urine tumorassociated trypsin inhibitor, CYFRA 21
1, and urinary bladder cancer antigen for detection of high-grade bladder carcinoma. Urology 2008, 72 (5), 1159–63. (22) Ward, D. G.; Nyangoma, S.; Joy, H.; Hamilton, E.; Wei, W.; Tselepis, C.; Steven, N.; Wakelam, M. J.; Johnson, P. J.; Ismail, T.; Martin, A. Proteomic profiling of urine for the detection of colon cancer. Proteome Sci. 2008, 6, 19. (23) Nagaraj, N.; Mann, M. Quantitative analysis of the intra- and inter-individual variability of the normal urinary proteome. J. Proteome Res. 2011, 10 (2), 637–45. (24) Harsha, H. C.; Molina, H.; Pandey, A. Quantitative proteomics using stable isotope labeling with amino acids in cell culture. Nat. Protoc. 2008, 3 (3), 505–16. (25) Mathivanan, S.; Ahmed, M.; Ahn, N. G.; Alexandre, H.; Amanchy, R.; Andrews, P. C.; Bader, J. S.; Balgley, B. M.; Bantscheff, M.; Bennett, K. L.; Bjorling, E.; Blagoev, B.; Bose, R.; Brahmachari, S. K.; Burlingame, A. S.; Bustelo, X. R.; Cagney, G.; Cantin, G. T.; Cardasis, H. L.; Celis, J. E.; Chaerkady, R.; Chu, F.; Cole, P. A.; Costello, C. E.; Cotter, R. J.; Crockett, D.; DeLany, J. P.; De Marzo, A. M.; DeSouza, L. V.; Deutsch, E. W.; Dransfield, E.; Drewes, G.; Droit, A.; Dunn, M. J.; Elenitoba-Johnson, K.; Ewing, R. M.; Van Eyk, J.; Faca, V.; Falkner, J.; Fang, X.; Fenselau, C.; Figeys, D.; Gagne, P.; Gelfi, C.; Gevaert, K.; Gimble, J. M.; Gnad, F.; Goel, R.; Gromov, P.; Hanash, S. M.; Hancock, W. S.; Harsha, H. C.; Hart, G.; Hays, F.; He, F.; Hebbar, P.; Helsens, K.; Hermeking, H.; Hide, W.; Hjerno, K.; Hochstrasser, D. F.; Hofmann, O.; Horn, D. M.; Hruban, R. H.; Ibarrola, N.; James, P.; Jensen, O. N.; Jensen, P. H.; Jung, P.; Kandasamy, K.; Kheterpal, I.; Kikuno, R. F.; Korf, U.; Korner, R.; Kuster, B.; Kwon, M. S.; Lee, H. J.; Lee, Y. J.; Lefevre, M.; Lehvaslaiho, M.; Lescuyer, P.; Levander, F.; Lim, M. S.; Lobke, C.; Loo, 2741

dx.doi.org/10.1021/pr2003038 |J. Proteome Res. 2011, 10, 2734–2743

Journal of Proteome Research J. A.; Mann, M.; Martens, L.; Martinez-Heredia, J.; McComb, M.; McRedmond, J.; Mehrle, A.; Menon, R.; Miller, C. A.; Mischak, H.; Mohan, S. S.; Mohmood, R.; Molina, H.; Moran, M. F.; Morgan, J. D.; Moritz, R.; Morzel, M.; Muddiman, D. C.; Nalli, A.; Navarro, J. D.; Neubert, T. A.; Ohara, O.; Oliva, R.; Omenn, G. S.; Oyama, M.; Paik, Y. K.; Pennington, K.; Pepperkok, R.; Periaswamy, B.; Petricoin, E. F.; Poirier, G. G.; Prasad, T. S.; Purvine, S. O.; Rahiman, B. A.; Ramachandran, P.; Ramachandra, Y. L.; Rice, R. H.; Rick, J.; Ronnholm, R. H.; Salonen, J.; Sanchez, J. C.; Sayd, T.; Seshi, B.; Shankari, K.; Sheng, S. J.; Shetty, V.; Shivakumar, K.; Simpson, R. J.; Sirdeshmukh, R.; Siu, K. W.; Smith, J. C.; Smith, R. D.; States, D. J.; Sugano, S.; Sullivan, M.; SupertiFurga, G.; Takatalo, M.; Thongboonkerd, V.; Trinidad, J. C.; Uhlen, M.; Vandekerckhove, J.; Vasilescu, J.; Veenstra, T. D.; Vidal-Taboada, J. M.; Vihinen, M.; Wait, R.; Wang, X.; Wiemann, S.; Wu, B.; Xu, T.; Yates, J. R.; Zhong, J.; Zhou, M.; Zhu, Y.; Zurbig, P.; Pandey, A. Human Proteinpedia enables sharing of human protein data. Nat. Biotechnol. 2008, 26 (2), 164–7. (26) Peri, S.; Navarro, J. D.; Amanchy, R.; Kristiansen, T. Z.; Jonnalagadda, C. K.; Surendranath, V.; Niranjan, V.; Muthusamy, B.; Gandhi, T. K.; Gronborg, M.; Ibarrola, N.; Deshpande, N.; Shanker, K.; Shivashankar, H. N.; Rashmi, B. P.; Ramya, M. A.; Zhao, Z.; Chandrika, K. N.; Padma, N.; Harsha, H. C.; Yatish, A. J.; Kavitha, M. P.; Menezes, M.; Choudhury, D. R.; Suresh, S.; Ghosh, N.; Saravana, R.; Chandran, S.; Krishna, S.; Joy, M.; Anand, S. K.; Madavan, V.; Joseph, A.; Wong, G. W.; Schiemann, W. P.; Constantinescu, S. N.; Huang, L.; Khosravi-Far, R.; Steen, H.; Tewari, M.; Ghaffari, S.; Blobe, G. C.; Dang, C. V.; Garcia, J. G.; Pevsner, J.; Jensen, O. N.; Roepstorff, P.; Deshpande, K. S.; Chinnaiyan, A. M.; Hamosh, A.; Chakravarti, A.; Pandey, A. Development of human protein reference database as an initial platform for approaching systems biology in humans. Genome Res. 2003, 13 (10), 2363–71. (27) Chaerkady, R.; Thuluvath, P. J.; Kim, M. S.; Nalli, A.; Vivekanandan, P.; Simmers, J.; Torbenson, M.; Pandey, A. O Labeling for a Quantitative Proteomic Analysis of Glycoproteins in Hepatocellular Carcinoma. Clin. Proteomics 2008, 4 (3
4), 137–155. (28) Keshava Prasad, T. S.; Goel, R.; Kandasamy, K.; Keerthikumar, S.; Kumar, S.; Mathivanan, S.; Telikicherla, D.; Raju, R.; Shafreen, B.; Venugopal, A.; Balakrishnan, L.; Marimuthu, A.; Banerjee, S.; Somanathan, D. S.; Sebastian, A.; Rani, S.; Ray, S.; Harrys Kishore, C. J.; Kanth, S.; Ahmed, M.; Kashyap, M. K.; Mohmood, R.; Ramachandra, Y. L.; Krishna, V.; Rahiman, B. A.; Mohan, S.; Ranganathan, P.; Ramabadran, S.; Chaerkady, R.; Pandey, A. Human Protein Reference Database--2009 update. Nucleic Acids Res. 2009, 37 (Database issue), D767–72. (29) Simpson, R. J.; Lim, J. W.; Moritz, R. L.; Mathivanan, S. Exosomes: proteomic insights and diagnostic potential. Expert Rev. Proteomics 2009, 6 (3), 267–83. (30) Pisitkun, T.; Shen, R. F.; Knepper, M. A. Identification and proteomic profiling of exosomes in human urine. Proc. Natl. Acad. Sci. U.S.A. 2004, 101 (36), 13368–73. (31) Gonzales, P. A.; Pisitkun, T.; Hoffert, J. D.; Tchapyjnikov, D.; Star, R. A.; Kleta, R.; Wang, N. S.; Knepper, M. A. Large-scale proteomics and phosphoproteomics of urinary exosomes. J. Am. Soc. Nephrol. 2009, 20 (2), 363–79. (32) Jia, L.; Zhang, L.; Shao, C.; Song, E.; Sun, W.; Li, M.; Gao, Y. An attempt to understand kidney’s protein handling function by comparing plasma and urine proteomes. PLoS One 2009, 4 (4), e5146. (33) Miller, C. An overview of the potassium channel family. Genome Biol. 2000, 1 (4), REVIEWS0004. (34) Trevino, L. R.; Yang, W.; French, D.; Hunger, S. P.; Carroll, W. L.; Devidas, M.; Willman, C.; Neale, G.; Downing, J.; Raimondi, S. C.; Pui, C. H.; Evans, W. E.; Relling, M. V. Germline genomic variants associated with childhood acute lymphoblastic leukemia. Nat. Genet. 2009, 41 (9), 1001–5. (35) Feng, Q.; Hawes, S. E.; Stern, J. E.; Wiens, L.; Lu, H.; Dong, Z. M.; Jordan, C. D.; Kiviat, N. B.; Vesselle, H. DNA methylation in tumor and matched normal tissues from non-small cell lung cancer patients. Cancer Epidemiol. Biomarkers Prev. 2008, 17 (3), 645–54.

ARTICLE

(36) Dietz, M. L.; Bernaciak, T. M.; Vendetti, F.; Kielec, J. M.; Hildebrand, J. D. Differential actin-dependent localization modulates the evolutionarily conserved activity of Shroom family proteins. J. Biol. Chem. 2006, 281 (29), 20542–54. (37) Schiaffino, M. V.; Bassi, M. T.; Rugarli, E. I.; Renieri, A.; Galli, L.; Ballabio, A. Cloning of a human homologue of the Xenopus laevis APX gene from the ocular albinism type 1 critical region. Hum. Mol. Genet. 1995, 4 (3), 373–82. (38) Prudkin, L.; Behrens, C.; Liu, D. D.; Zhou, X.; Ozburn, N. C.; Bekele, B. N.; Minna, J. D.; Moran, C.; Roth, J. A.; Ji, L.; Wistuba, I. I. Loss and reduction of FUS1 protein expression is a frequent phenomenon in the pathogenesis of lung cancer. Clin. Cancer Res. 2008, 14 (1), 41–7. (39) Storch, J.; McDermott, L. Structural and functional analysis of fatty acid-binding proteins. J. Lipid Res. 2009, 50 (Suppl), S126–31. (40) Noiri, E.; Doi, K.; Negishi, K.; Tanaka, T.; Hamasaki, Y.; Fujita, T.; Portilla, D.; Sugaya, T. Urinary fatty acid-binding protein 1: an early predictive biomarker of kidney injury. Am. J. Physiol. Renal Physiol. 2009, 296 (4), F669–79. (41) Zimmermann-Ivol, C. G.; Burkhard, P. R.; Le Floch-Rohr, J.; Allard, L.; Hochstrasser, D. F.; Sanchez, J. C. Fatty acid binding protein as a serum marker for the early diagnosis of stroke: a pilot study. Mol. Cell. Proteomics 2004, 3 (1), 66–72. (42) Matos, J. M.; Witzmann, F. A.; Cummings, O. W.; Schmidt, C. M. A pilot study of proteomic profiles of human hepatocellular carcinoma in the United States. J. Surg. Res. 2009, 155 (2), 237–43. (43) Perroud, B.; Ishimaru, T.; Borowsky, A. D.; Weiss, R. H. Gradedependent proteomics characterization of kidney cancer. Mol. Cell. Proteomics 2009, 8 (5), 971–85. (44) Pang, J.; Liu, W. P.; Liu, X. P.; Li, L. Y.; Fang, Y. Q.; Sun, Q. P.; Liu, S. J.; Li, M. T.; Su, Z. L.; Gao, X. Profiling protein markers associated with lymph node metastasis in prostate cancer by DIGE-based proteomics analysis. J. Proteome Res. 2010, 9 (1), 216–26. (45) Kondo, M.; Ji, L.; Kamibayashi, C.; Tomizawa, Y.; Randle, D.; Sekido, Y.; Yokota, J.; Kashuba, V.; Zabarovsky, E.; Kuzmin, I.; Lerman, M.; Roth, J.; Minna, J. D. Overexpression of candidate tumor suppressor gene FUS1 isolated from the 3p21.3 homozygous deletion region leads to G1 arrest and growth inhibition of lung cancer cells. Oncogene 2001, 20 (43), 6258–62. (46) Genander, M.; Frisen, J., Ephrins and Eph receptors in stem cells and cancer. Curr. Opin. Cell Biol. 2010, 22 (5), 611-6. (47) Giaginis, C.; Tsourouflis, G.; Zizi-Serbetzoglou, A.; Kouraklis, G.; Chatzopoulou, E.; Dimakopoulou, K.; Theocharis, S. E. Clinical significance of ephrin (eph)-A1, -A2, -a4, -a5 and -a7 receptors in pancreatic ductal adenocarcinoma. Pathol. Oncol. Res. 2010, 16 (2), 267–76. (48) Alvarez, H.; Montgomery, E. A.; Karikari, C.; Canto, M.; Dunbar, K. B.; Wang, J. S.; Feldmann, G.; Hong, S. M.; Haffner, M. C.; Meeker, A. K.; Holland, S. J.; Yu, J.; Heckrodt, T. J.; Zhang, J.; Ding, P.; Goff, D.; Singh, R.; Roa, J. C.; Marimuthu, A.; Riggins, G. J.; Eshleman, J. R.; Nelkin, B. D.; Pandey, A.; Maitra, A. The Axl receptor tyrosine kinase is an adverse prognostic factor and a therapeutic target in esophageal adenocarcinoma. Cancer Biol. Ther. 2010, 10 (10), 10. (49) Gilbertson, R. J.; Clifford, S. C. PDGFRB is overexpressed in metastatic medulloblastoma. Nat. Genet. 2003, 35 (3), 197–8. (50) Gong, J.; Morishita, A.; Kurokohchi, K.; Tani, J.; Kato, K.; Miyoshi, H.; Inoue, H.; Kobayashi, M.; Liu, S.; Murota, M.; Muramatsu, A.; Izuishi, K.; Suzuki, Y.; Yoshida, H.; Uchida, N.; Deguchi, K.; Iwama, H.; Ishimaru, I.; Masaki, T. Use of protein array to investigate receptor tyrosine kinases activated in gastric cancer. Int. J. Oncol. 2010, 36 (1), 101–6. (51) Schiess, R.; Wollscheid, B.; Aebersold, R. Targeted proteomic strategy for clinical biomarker discovery. Mol. Oncol. 2009, 3 (1), 33–44. (52) Ahn, Y. H.; Lee, J. Y.; Lee, J. Y.; Kim, Y. S.; Ko, J. H.; Yoo, J. S. Quantitative analysis of an aberrant glycoform of TIMP1 from colon cancer serum by L-PHA-enrichment and SISCAPA with MRM mass spectrometry. J. Proteome Res. 2009, 8 (9), 4216–24. (53) Kashyap, M. K.; Harsha, H. C.; Renuse, S.; Pawar, H.; Sahasrabuddhe, N. A.; Kim, M. S.; Marimuthu, A.; Keerthikumar, S.; Muthusamy, B.; Kandasamy, K.; Subbannayya, Y.; Prasad, T. S.; 2742

dx.doi.org/10.1021/pr2003038 |J. Proteome Res. 2011, 10, 2734–2743

Journal of Proteome Research

ARTICLE

Mahmood, R.; Chaerkady, R.; Meltzer, S. J.; Kumar, R. V.; Rustgi, A. K.; Pandey, A. SILAC-based quantitative proteomic approach to identify potential biomarkers from the esophageal squamous cell carcinoma secretome. Cancer Biol. Ther. 2010, 10, 8. (54) Varambally, S.; Laxman, B.; Mehra, R.; Cao, Q.; Dhanasekaran, S. M.; Tomlins, S. A.; Granger, J.; Vellaichamy, A.; Sreekumar, A.; Yu, J.; Gu, W.; Shen, R.; Ghosh, D.; Wright, L. M.; Kladney, R. D.; Kuefer, R.; Rubin, M. A.; Fimmel, C. J.; Chinnaiyan, A. M. Golgi protein GOLM1 is a tissue and urine biomarker of prostate cancer. Neoplasia 2008, 10 (11), 1285–94. (55) Mufson, E. J.; Wuu, J.; Counts, S. E.; Nykjaer, A. Preservation of cortical sortilin protein levels in MCI and Alzheimer’s disease. Neurosci. Lett. 2011, 471 (3), 129–33. (56) Samani, N. J.; Erdmann, J.; Hall, A. S.; Hengstenberg, C.; Mangino, M.; Mayer, B.; Dixon, R. J.; Meitinger, T.; Braund, P.; Wichmann, H. E.; Barrett, J. H.; Konig, I. R.; Stevens, S. E.; Szymczak, S.; Tregouet, D. A.; Iles, M. M.; Pahlke, F.; Pollard, H.; Lieb, W.; Cambien, F.; Fischer, M.; Ouwehand, W.; Blankenberg, S.; Balmforth, A. J.; Baessler, A.; Ball, S. G.; Strom, T. M.; Braenne, I.; Gieger, C.; Deloukas, P.; Tobin, M. D.; Ziegler, A.; Thompson, J. R.; Schunkert, H. Genomewide association analysis of coronary artery disease. N. Engl. J. Med. 2007, 357 (5), 443–53. (57) Kathiresan, S.; Voight, B. F.; Purcell, S.; Musunuru, K.; Ardissino, D.; Mannucci, P. M.; Anand, S.; Engert, J. C.; Samani, N. J.; Schunkert, H.; Erdmann, J.; Reilly, M. P.; Rader, D. J.; Morgan, T.; Spertus, J. A.; Stoll, M.; Girelli, D.; McKeown, P. P.; Patterson, C. C.; Siscovick, D. S.; O’Donnell, C. J.; Elosua, R.; Peltonen, L.; Salomaa, V.; Schwartz, S. M.; Melander, O.; Altshuler, D.; Ardissino, D.; Merlini, P. A.; Berzuini, C.; Bernardinelli, L.; Peyvandi, F.; Tubaro, M.; Celli, P.; Ferrario, M.; Fetiveau, R.; Marziliano, N.; Casari, G.; Galli, M.; Ribichini, F.; Rossi, M.; Bernardi, F.; Zonzin, P.; Piazza, A.; Mannucci, P. M.; Schwartz, S. M.; Siscovick, D. S.; Yee, J.; Friedlander, Y.; Elosua, R.; Marrugat, J.; Lucas, G.; Subirana, I.; Sala, J.; Ramos, R.; Kathiresan, S.; Meigs, J. B.; Williams, G.; Nathan, D. M.; MacRae, C. A.; O’Donnell, C. J.; Salomaa, V.; Havulinna, A. S.; Peltonen, L.; Melander, O.; Berglund, G.; Voight, B. F.; Kathiresan, S.; Hirschhorn, J. N.; Asselta, R.; Duga, S.; Spreafico, M.; Musunuru, K.; Daly, M. J.; Purcell, S.; Voight, B. F.; Purcell, S.; Nemesh, J.; Korn, J. M.; McCarroll, S. A.; Schwartz, S. M.; Yee, J.; Kathiresan, S.; Lucas, G.; Subirana, I.; Elosua, R.; Surti, A.; Guiducci, C.; Gianniny, L.; Mirel, D.; Parkin, M.; Burtt, N.; Gabriel, S. B.; Samani, N. J.; Thompson, J. R.; Braund, P. S.; Wright, B. J.; Balmforth, A. J.; Ball, S. G.; Hall, A. S.; Schunkert, H.; Erdmann, J.; Linsel-Nitschke, P.; Lieb, W.; Ziegler, A.; Konig, I.; Hengstenberg, C.; Fischer, M.; Stark, K.; Grosshennig, A.; Preuss, M.; Wichmann, H. E.; Schreiber, S.; Schunkert, H.; Samani, N. J.; Erdmann, J.; Ouwehand, W.; Hengstenberg, C.; Deloukas, P.; Scholz, M.; Cambien, F.; Reilly, M. P.; Li, M.; Chen, Z.; Wilensky, R.; Matthai, W.; Qasim, A.; Hakonarson, H. H.; Devaney, J.; Burnett, M. S.; Pichard, A. D.; Kent, K. M.; Satler, L.; Lindsay, J. M.; Waksman, R.; Knouff, C. W.; Waterworth, D. M.; Walker, M. C.; Mooser, V.; Epstein, S. E.; Rader, D. J.; Scheffold, T.; Berger, K.; Stoll, M.; Huge, A.; Girelli, D.; Martinelli, N.; Olivieri, O.; Corrocher, R.; Morgan, T.; Spertus, J. A.; McKeown, P.; Patterson, C. C.; Schunkert, H.; Erdmann, E.; Linsel-Nitschke, P.; Lieb, W.; Ziegler, A.; Konig, I. R.; Hengstenberg, C.; Fischer, M.; Stark, K.; Grosshennig, A.; Preuss, M.; Wichmann, H. E.; Schreiber, S.; Holm, H.; Thorleifsson, G.; Thorsteinsdottir, U.; Stefansson, K.; Engert, J. C.; Do, R.; Xie, C.; Anand, S.; Kathiresan, S.; Ardissino, D.; Mannucci, P. M.; Siscovick, D.; O’Donnell, C. J.; Samani, N. J.; Melander, O.; Elosua, R.; Peltonen, L.; Salomaa, V.; Schwartz, S. M.; Altshuler, D. Genome-wide association of early-onset myocardial infarction with single nucleotide polymorphisms and copy number variants. Nat. Genet. 2009, 41 (3), 334–41.

2743

dx.doi.org/10.1021/pr2003038 |J. Proteome Res. 2011, 10, 2734–2743